Journal of Surgery Concepts & Practice >

2026 , Vol. 31 >Issue 03: 185 - 190

DOI: https://doi.org/10.16139/j.1007-9610.2026.03.02

Experts forum

Large language models empower precision decision-making in breast surgery and whole-cycle patient management

  • ZHU Nengjun ,
  • CAO Jian ,
  • CHEN Xiaosong ,
  • ZHU Siji
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  • 1 School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
    2 School of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China
    3 Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China

Received date: 2025-04-22

  Online published: 2026-08-11

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Abstract

Early-stage breast cancer diagnosis and treatment are characterized by long disease course, high heterogeneity, multi-source and heterogeneous data, and dynamic evolving decision-making. The traditional multidisciplinary team (MDT) decision-making model faces challenges such as low efficiency and fragmented whole-cycle management. Large language models (LLMs) provide a new approach to solving these problems by virtue of their advantages in processing unstructured text, integrating multi-source data, and assisting clinical reasoning. This paper elaborated on the application value of LLMs in breast cancer MDT decision-making, focused on analyzing three core methods: personalized treatment recommendation agents, multi-agent collaboration, and clinical decision-making evidence generation and fusion. Furthermore, it proposed future development strategies across four dimensions: dynamic update of clinical knowledge bases, in-depth integration of case databases and models, human-machine collaborative case management, and OpenClaw-style intelligent physician assistant, providing references for promoting whole-cycle precise diagnosis and treatment of breast cancer and optimizing the MDT model.

Key words： Large language model (LLM); Breast cancer; Multidisciplinary team (MDT) decision-making; Precise diagnosis and treatment; Whole-cycle management

Cite this article

ZHU Nengjun , CAO Jian , CHEN Xiaosong , ZHU Siji . Large language models empower precision decision-making in breast surgery and whole-cycle patient management[J]. Journal of Surgery Concepts & Practice, 2026 , 31(03) : 185 -190 . DOI: 10.16139/j.1007-9610.2026.03.02

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